3-dimensional representations of post-contrast enhanced brain lesions

ABSTRACT

3D MRI images of the brain may be created and acquired. After administration of contrast, brain lesions and other abnormalities may be identified and isolated from the 3D MRI images, with each lesion serving as a region of interest (ROI). 3D region of contrast enhancement images may be created from segmented 3D MRI images and different regions of contrast enhancement of the brain lesion may be depicted. Saved regions of contrast enhancement may be converted into stereolithography format, maximum intensity projection (MIP) images, and/or orthographic projection images. Data corresponding to these resulting 3D region of contrast enhancement images may be used to create 3D printed models of the isolated region of contrast enhancement. Analysis of the 3D brain region of contrast enhancement images and the 3D printed region of contrast enhancement models may enable a more efficient and accurate way of determining brain lesion risk factors and effective treatment regimens.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a national phase application under 35 U.S.C. § 371of International Application No. PCT/US2019/0619652, filed Feb. 26,2019, which claims priority to U.S. Provisional Patent Application No.62/635,288, filed Feb. 26, 2018, the contents of each of which areincorporated by reference in their entireties.

FIELD OF THE INVENTION

This disclosure relates generally to methods, apparatuses, and systemsfor creating 3-dimensional (3D) representations exhibiting spatial,surface, and structural characteristics of post-contrast enhanced brainlesions.

DESCRIPTION OF RELATED ART

The diagnosis of multiple sclerosis (MS) requires the fulfillment ofboth clinical and radiological criteria. This may include adetermination of key radiological tenets such as a requisite number oflesions having a specific character (i.e. size, shape, and morphology)and spatial distribution patterns with involvement of periventricular,juxtacortical, infratentorial, and spinal cord regions. The effectiveapplication of the existing dissemination in space criteria may behindered by the highly sensitive nature of MRI technology, theheterogeneity of lesions resulting from a variety of medical conditions,concomitant radiological features resulting from age-related changes anddisease, and the lack of additional radiological characteristics beyondtwo-dimensional descriptions.

At present, the diagnosis of MS is usually made through the use of2-dimensional (2D) MRI images. The implementation of certain imagingmetrics, including the use of quantitative phase imaging, has improvedlesion specificity. This may highlight the presence of centralvasculature within lesions and distinct peripheral rings, suggesting thepresence of iron within macrophages. The use of fluid-attenuatedinversion recovery (FLAIR) MRI at 3 Tesla (T) and T2-weighted andsusceptibility weighted imaging (SWI) at 7 T in larger patient groupshas also been utilized to better characterize MS from non-MS lesions. Apreviously identified threshold of >40% was described for improving thespecificity of MS lesions. However, this technique has been limited bythe lack of appreciation of the central vessel in all orthogonal planesof view and the abundance of vessels intersecting lesions within thesupratentorial region. Beyond these efforts, peripheral regions ofhypointensity, presumed to be related to the presence of iron withinmacrophages, have also been described in MS patients.

Glioblastoma multiforme (GBM) is the most frequent high-grade gliomadiagnosed annually, having a global incidence of 0.59-3.69 per 100,000persons. Despite advances in molecular profiling, neuroradiology, andavailable treatments, the approximate 5-year survival rate of those withGBM is only 5%. Magnetic resonance imaging (MRI) data is invaluable inthe surveillance of disease; however, determining the origin of new oradvancing regions of contrast enhancement and the association of thesefindings with radiation necrosis, tumor recurrence, immune activity, ora combination of these factors represents a key area of clinicalmanagement given the prognostic and therapeutic implications.

Currently, there is no clear evidentiary standard regarding radiologicaldefinitions of disease advancement and the value of post-contrastfeatures in identifying malignant transformation remains controversial.When observed, contrast enhancement signifies blood brain barrierpermeability that may be the result of varied etiologies. Previous datasuggests that tumor cell density and cellular mitosis appear to behighest in regions of contrast enhancement. The significance of distinctradiological patterns of enhancement were previously investigated;however, a remarkable prognostic signature has not yet been identified.In addition, the observed contrast enhanced data may be influenced byO⁶-methylguanine-DNA methyltransferase (MGMT) promoter methylationstatus. The routine evaluation of post-contrast imaging features isconfined to 2-dimensional (2D), forced-perspective views. These 2D viewslimit the understanding of true spatial, surface, and structuralpatterns of 3D brain lesions. The lack of reliable contrast enhancementsignatures may be the result of this limited approach.

Morphological, functional, and metabolic features have been studied inthe past including 3,4-dihydroxy-6-[18F]-fluoro-L-phenylalanine(18-DOPA) PET, O-(2-18F-Fluoroethyl)-L-Tyrosine PET/MRI, apparentdiffusion coefficient (ADC) maps, and susceptibility-weighted MRI anddynamic susceptibility contrast (DSC) perfusion-weight imaging (PWI) inefforts to identify radiological patterns associated with true tumoradvancement. Studies focused on the value of contrast enhancementradiological phenotypes have also been explored. Texture analysis ofpost contrast imaging sequences has been used to differentiate betweenlow and high grade gliomas pre-surgical intervention as well as betweendifferent types of brain tumors when incorporated into machine learningalgorithms. The prognostic significance of 2D contrast patterns having“patchy and faint”, “nodular-like”, and “ring-like” characteristics inlow grade gliomas was previously assessed with the nodular-like patternand time-progressive contrast enhancement associated with poor prognosisfollowing univariate analysis. Similar focal and nodular patterns of T1contrast enhancement were also found to correlate with tumor recurrencein high grade gliomas, while thin, linear enhancement was associatedwith radiation necrosis. However, these investigations were limited to2D MRI views which may account for the lack of an identified robustsignal.

SUMMARY

This disclosure includes embodiments of methods, apparatuses, andsystems for creating 3-dimensional (3D) representations exhibitingspatial, surface, and structural characteristics of post-contrastenhanced brain lesions. Some embodiments comprise a computer systemhaving at least one processor that may be configured to receive one ormore 3D images of a brain; enable an identification of one or moreregions of contrast enhancement in the one or more 3D images of thebrain; enable a segmentation of the one or more 3D images, thesegmentation enabling an isolation of the one or more regions ofcontrast enhancement; enable a creation of one or more 3D region ofcontrast enhancement images based on the segmentation, the one or more3D region of contrast enhancement images comprising one or more regionof contrast enhancement characteristics; enable a comparison of the oneor more region of contrast enhancement characteristics of the one ormore 3D region of contrast enhancement images with one or morepredetermined region of contrast enhancement characteristics; and enablea determination of a category of the one or more region of contrastenhancement based on a match between the one or more region of contrastenhancement characteristics of the one or more 3D region of contrastenhancement images and the one or more predetermined region of contrastenhancement characteristics. In some embodiments, the at least oneprocessor may be able to communicate with a memory source and/ornon-transitory computer readable medium to receive one or moreinstructions enabling the at least one processor to perform the actionsdisclosed above. In some embodiments, the at least one processor may beactively performing the actions disclosed above based on one or moreinstructions received from a memory source and/or non-transitorycomputer readable medium. In some embodiments, the at least oneprocessor may be hardwired in such a way as to have the ability toperform and/or actually perform the actions disclosed above.

In some embodiments, the computer system may be further configured toenable the sending of 3D representation data corresponding to the one ormore 3D region of contrast enhancement images, the 3D representationdata configured to enable a creation of one or more physical 3Drepresentations of the one or more regions of contrast enhancement. Theone or more 3D images may comprise one or more maximum intensityprojection (MIP) images, the MIP images configured to enable 3D spatialvisualization of the regions of contrast enhancement. In someembodiments, the one or more 3D region of contrast enhancement imagesmay comprise one or more orthographic projections in stereolithographicformat. In some embodiments, the one or more physical 3D representationscomprise may be fused filament 3D printed models. The one or more 3Dimages of the brain may comprise one or more isotropic magneticresonance imaging (MRI) images. In some embodiments, the one or moreregion of contrast enhancement characteristics may comprise one or moreof geometric characteristics, surface characteristics, and structuralcharacteristics. In some embodiments, the one or more predeterminedregion of contrast enhancement characteristics may correspond to one ormore lesion characteristics associated with one or more disease riskfactors, In some embodiments, the at least one processor is furtherconfigured to use machine learning to generate at least one descriptionfor the one or more region of contrast enhancement characteristics andassociate the at least one description with the category of the one ormore region of contrast enhancement.

Some embodiments of the present methods include a method of creating3-dimensional (3D) representations of post-contrast enhanced brainlesions that may comprise receiving, by a computer system comprising atleast one processor, one or more 3D images of a brain; enabling, by thecomputer system, an identification of one or more regions of contrastenhancement in the one or more 3D images of the brain; enabling, by thecomputer system, a segmentation of the one or more 3D images, thesegmentation enabling an isolation of the one or more regions ofcontrast enhancement; enabling, by the computer system, a creation ofone or more 3D region of contrast enhancement images based on thesegmentation, the one or more 3D region of contrast enhancement imagescomprising one or more region of contrast enhancement characteristics;enabling, by the computer system, a comparison of the one or more regionof contrast enhancement characteristics of the one or more 3D region ofcontrast enhancement images with one or more predetermined region ofcontrast enhancement characteristics; and enabling, by the computersystem, a determination of a category of the one or more regions ofcontrast enhancement based on a match between the one or more region ofcontrast enhancement characteristics of the one or more 3D region ofcontrast enhancement images and the one or more predetermined region ofcontrast enhancement characteristics.

Some embodiments of the present methods of characterizing and/orpredicting behavior of a brain lesion include receiving a first set ofone or more 3D images of a brain generated at a first time; receiving asecond set of one or more 3D images of the brain generated at a secondtime that is later than the first time; for each of the first and secondsets, and with at least one processor, identifying one or more regionsof contrast enhancement in the set, segmenting the set to isolate theregion(s) of contrast enhancement, and creating, based on the isolatedregion(s) of contrast enhancement, one or more 3D region of contrastenhancement representations comprising one or more region of contrastenhancement characteristics; and comparing one or more of the region ofcontrast enhancement characteristic(s) associated with the first set toone or more of the region of contrast enhancement characteristic(s)associated with the second set. Some methods comprise creating a 3Dregion of contrast enhancement representation that is indicative of oneor more differences between one or more of the region of contrastenhancement characteristic(s) associated with the first set, and one ormore of the region of contrast enhancement characteristic(s) associatedwith the second set.

The terms “a” and “an” are defined as one or more unless this disclosureexplicitly requires otherwise. The term “substantially” is defined aslargely but not necessarily wholly what is specified (and includes whatis specified; e.g., substantially 90 degrees includes 90 degrees andsubstantially parallel includes parallel), as understood by a person ofordinary skill in the art. In any disclosed embodiment, the terms“substantially,” “approximately,” and “about” may be substituted with“within [a percentage] of” what is specified, where the percentageincludes 0.1, 1, 5, and 10 percent.

The terms “comprise” (and any form of comprise, such as “comprises” and“comprising”), “have” (and any form of have, such as “has” and“having”), “include” (and any form of include, such as “includes” and“including”) and “contain” (and any form of contain, such as “contains”and “containing”) are open-ended linking verbs. As a result, a system,or a component of a system, that “comprises,” “has,” “includes” or“contains” one or more elements or features possesses those one or moreelements or features, but is not limited to possessing only thoseelements or features. Likewise, a method that “comprises,” “has,”“includes” or “contains” one or more steps possesses those one or moresteps, but is not limited to possessing only those one or more steps.Additionally, terms such as “first” and “second” are used only todifferentiate structures or features, and not to limit the differentstructures or features to a particular order.

Any embodiment of any of the disclosed methods, systems, systemcomponents, or method steps can consist of or consist essentiallyof—rather than comprise/include/contain/have—any of the describedelements, steps, and/or features. Thus, in any of the claims, the term“consisting of” or “consisting essentially of” can be substituted forany of the open-ended linking verbs recited above, in order to changethe scope of a given claim from what it would otherwise be using theopen-ended linking verb.

The feature or features of one embodiment may be applied to otherembodiments, even though not described or illustrated, unless expresslyprohibited by this disclosure or the nature of the embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

The following drawings illustrate by way of example and not limitation.For the sake of brevity and clarity, every feature of a given method orsystem is not always labeled in every figure related to that method orsystem. Identical reference numbers do not necessarily indicate anidentical feature. Rather, the same reference number may be used toindicate a similar feature or a feature with similar functionality, asmay non-identical reference numbers.

FIG. 1 depicts an exemplary 3D imaging and post-contrast brain lesionrepresentation system according to an embodiment of the disclosure.

FIG. 2 depicts an exemplary method for creating 3D representations ofpost-contrast brain lesion characteristics according to an embodiment ofthe disclosure.

FIGS. 3A-C depict exemplary 2D MRI images showing an administration of acontrast substance into a patient brain and a post-contrast brainlesion.

FIGS. 4A-B depict exemplary 2D MRI images showing an administration of acontrast substance into a patient brain and a post-contrast brain lesionaccording to an embodiment of the disclosure.

FIGS. 5A-B depict exemplary 3D brain lesion images that may beconstructed of the brain lesions shown in FIGS. 4A-B according to anembodiment of the disclosure.

FIGS. 6A-B depict exemplary 3D post-contrast brain lesion images thatmay be constructed of the brain lesions shown in FIGS. 5A-B according toan embodiment of the disclosure.

FIGS. 7A-D depict exemplary experimental analysis results from animplementation of the systems and methods described by an embodiment ofthe disclosure.

FIGS. 8A-D depict exemplary experimental analysis results from animplementation of the systems and methods described by an embodiment ofthe disclosure.

FIG. 9 depicts an exemplary method for using 3D representations of apost-contrast brain lesion to identify one or more changes in one ormore of the lesion's characteristics over time.

FIGS. 10A-10B are sagittal MRI images of a post-contrast brain lesiontaken at a first time and a second, later time, respectively.

FIG. 11 is a 3D representation of the brain lesion of FIGS. 10A and 10B,indicating changes in its characteristics between the first and secondtimes.

FIGS. 12A-12B are MRI images of a post-contrast brain lesion taken at afirst time and a second, later time, respectively.

FIG. 12C is a simulated MRI image showing the brain lesion of FIGS.12A-12B at a third time that is later than the second time, as predictedusing an embodiment of the disclosure.

FIG. 12D is an MRI image of the brain lesion of FIGS. 12A-12B at thethird time.

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

Utilization of 3-dimensional (3D) methods may provide a betterunderstanding of what is observed on conventional 2D images along withnew insights into the observed radiological features resulting fromalterations in blood brain barrier physiology. This disclosure describesa practical and innovative approach to understanding 3D spatial,surface, and structural differences from brain MRI post-gadolinium datato improve the ability to differentiate between the effects oftherapeutic interventions and disease progression in GBM patients. These3D imaging and analytical techniques may easily be incorporated intostudies for direct clinical application.

The disclosed methods can be applied to MRI contrast enhancement datathat can then demonstrate remarkable 3D spatial, structural, and surfacefeatures between neurologically stable GBM patients and those withdisease advancement. Using the disclosed systems and methods, sphericalshapes and greater circumferential fullness can be identified in thosepatients with documented neurological decline suggesting the value ofpost-contrast 3D morphological data in identifying patients that requiretreatment regimen changes. There also appears to be a trend towardsdifferences in the post-contrast enhanced tumor shell width and meanenhanced voxel intensities between clinical states. Additionally, theapplication of machine learning techniques using multi-parametric MRIfeatures to identify tumor recurrence from pseudoprogression representsearly stages of technical advancements that may result in more sensitiveand specific solutions.

The embodiments discussed below describe systems, apparatus, and methodsfor creating 3-dimensional (3D) representations exhibiting shape andsurface characteristics of post-contrast brain lesions/tumors. Morespecifically, the embodiments discussed below present 3D spatialvisualization of the entire brain lesion and, more specifically, ofparticular regions of contrast enhancement of the brain lesion. These 3Dimages can provide geometric and surface characteristic data of theregions of contrast enhancement of brain tumors in comparison to certaindisease states that may improve tumor risk and treatment determinationsby leveraging imaging techniques that can be implemented into clinicallyacquired studies for direct clinical application. The embodimentsdiscussed below also describe integrating the use of 3D printingsoftware and hardware, allowing for tactile review of the observedfindings on MRI to further elucidate the geometric and surfacecharacteristics between varying region of contrast enhancementcategories.

Referring now to the drawings, FIG. 1 depicts an exemplary 3D imagingand brain lesion or tumor representation system 100 according to anembodiment of the disclosure. In the embodiment shown, an MRI device 102may be provided. The MRI device 102 may be a 3D MRI device, or one ormore MRI devices providing both 2D and 3D imaging capabilities. Aprocessing device 104 may be capable of receiving 3D images taken by theMRI device. Processing device 104 may be a part of a computer systemthat may include standard components such as a hard drive, monitor,printer, keyboard, and mouse, among others, that may enable a user tointeract with the processing device 104. In the embodiment shown,processing device 104 may include one or more of a segmentationapplication 106, a 3D imaging application 108, and one or more databases110. In some embodiments, segmentation application 106 may be configuredto receive one or more MRI images from MRI device 102, segment the oneor more MRI images into one or more regions, and enable a selection ofone or more regions. These selected regions may be referred to asregions of interest (ROI). In some embodiments, the selection of ROI maybe done automatically by processing device 104. In some embodiments, theselection of ROI may be done by a user.

In some embodiments, the selected ROI may be exported by segmentationapplication 106 and imported into 3D image application 108. In someembodiments, 3D image application 108 may generate one or more 3Dmaximum intensity projection (MIP) images of the selected ROI. In someembodiments, the selected ROI may correspond to one or more regions ofcontrast enhancement of a brain lesion or tumor. In some embodiments,the selected ROI may be converted to stereolithography (.stl) formatand/or displayed as 3D orthographic images to enable orthographic views.The one or more 3D images may be displayed to a user and 3D imageapplication 108 may enable a user to view and manipulate the one or more3D images. In some embodiments, image manipulation capabilities mayinclude capabilities to rotate, zoom, mark, color, and select the one ormore images. In some embodiments, one or more databases 110 may containinformation corresponding to various brain lesion characteristics andphysiology. Examples of these brain lesion characteristics may includeshape or geometric characteristics, size characteristics, topographicalcharacteristics, volume characteristics, surface area characteristicsand the like. In some embodiments, the brain lesion characteristics maybe associated with one or more stratified categories of risk and/ortreatment. Examples of these stratified categories may includecharacteristics that differentiate clinical stability versus diseaseadvancement, characteristics that predict a more aggressive or lessaggressive disease course, characteristics associated with varyingdegrees of injury, characteristics related to positive or negativetreatment responses, characteristics that differentiate between diseasetypes, and characteristics that differentiate disease advancement fromtreatment effects. In some embodiments, processing device 104 may beconfigured to send data corresponding to the one or more 3D images to a3D printing device 112. 3D printing device 112 may create a 3D physicalrepresentation of the received one or more 3D images.

FIG. 2 depicts an exemplary method 200 for creating 3D representationsof brain lesion/tumor ROI according to an embodiment of the disclosure.In one embodiment of the disclosure, method 200 may be implemented bysystem 100. In the embodiment shown in FIG. 2 , method 200 may begin atstep 204 by creating one or more 3D MRI images of a brain lesion. Insome embodiments, the one or more 3D MRI images are created by receivingone or more 3D MRI images of a patient's brain, segmenting the one ormore 3D MRI brain images into one or more ROI corresponding to a brainlesion/tumor, and creating a 3D MRI image of the ROI.

Method 200 may continue at step 208 by receiving the one or more 3D MRIimages of the ROI corresponding to one or more isolated brainlesions/tumors. Method 200 may continue at step 212 by detecting one ormore regions of contrast enhancement of the received one or more 3D MRIimages of the isolated brain lesion. Before a patient receives the 3DMRI, the patient can be administered a contrast substance (e.g.,gadolinium complexes, gadolinium compounds, a mixture including adye/pigment) that will enter the bloodstream and travel to the brainlesion. In some embodiments, the contrast substance can be aparamagnetic agent. Once the contrast substance reaches the brainlesion, it may disperse at different concentrations into the varioussections of the brain lesion based on the rate of growth, structuralcharacteristics, and degree of blood brain barrier permeability of thebrain lesion. For example, a higher concentration of the contrastsubstance may be drawn to an area of the brain lesion exhibiting a highgrowth rate, having a large number of blood vessels, or a weakenedstructural integrity. A lower concentration of the contrast substancemay be drawn to an area of the brain lesion that is less physiologicallyactive due to reduced tumor growth and/or reduced permeability of theblood brain barrier.

Method 200 may continue at step 216 by segmenting the received one ormore 3D MRI images. In some embodiments, segmenting step 216 may includesegmenting the one or more MRI images into the one or more regions ofcontrast enhancement. The one or more regions of contrast enhancementmay correspond to different distinct sections of the one or more brainlesions. In some embodiments, regions of contrast enhancement may beselected in 3D format using an MIP 3D file. In this way, the computersystem and/or a user may manipulate a 3D object in 2D space and mayselect one or more regions of contrast enhancement for further analysis.In another embodiment, regions of contrast enhancement may beautomatically selected in 3D format from the 3D MRI images by thecomputer system and modified manually by the user. Isolating regions ofcontrast enhancement from 3D MRI images of brain lesions may allow for abetter appreciation of brain lesion characteristics, risk factorsassociated with those characteristics, and possible treatments. In a 2Dview, a variety of signals may influence pixel intensities that mayresult in pixel misclassification. As a result, creating 3D models from2D ROI selections results in inaccurate representations. Isolatinglesions from 3D images may overcome some of these shortcomings of 2Dlesion isolation. In some embodiments, the different segments of the oneor more MRI images may be displayed in different colors or displayintensities.

Method 200 may continue at step 220 by creating one or more 3D images ofisolated regions of contrast enhancement. In some embodiments, the oneor more 3D images may be orthographic images or MIP images. In someembodiments, method 200 may continue at step 224 by enabling theanalysis of one or more brain lesion or region of contrast enhancementcharacteristics. For example, a computer system may analyze the one ormore images to determine one or more characteristics of the brain lesionand/or certain regions of contrast enhancement of the brain lesion. Auser may also analyze the one or more images by interacting with thecomputer system. In some embodiments, metadata may be used to denote atype or category of a region of contrast enhancement characteristic. Insome embodiments, region of contrast enhancement characteristics mayinclude geometric characteristics. Geometric characteristics may provideinsights into a size and shape of a brain lesion. Examples of geometriccharacteristics may include lesion symmetry/asymmetry, surfacemorphology (e.g., complex surface features), the existence of lobesand/or protrusions, and other shape characteristics (e.g., amorphous,ovoid). In some embodiments, region of contrast enhancementcharacteristics may include surface characteristics. Surfacecharacteristics may provide insights into lesion surface traits andlesion properties not associated with geometry. Examples of surfacecharacteristics may include differences in the contrast enhanced shell,the existence of surface microstructures, surface topography (e.g.,steepness/sheerness of surface peaks and valleys), surfaceirregularities (e.g., fullness of the outer contrast enhanced shell,complex surface morphology), and a non-uniform distribution of mass ofthe lesion. In some embodiments, the computer system may engage inmachine learning to generate descriptive surface, shape, and signalcharacteristics from the entire lesion or sections within lesions inorder to more efficiently and accurately classify lesion types.

Method 200 may continue at step 228 by enabling a determination of aregion of contrast enhancement category. In some embodiments, a computersystem may compare the one or more region of contrast enhancementcharacteristics to one or more previously stored region of contrastenhancement characteristics to determine possible matches. In someembodiments, one or more previously stored region of contrastenhancement characteristics may correspond to one or more region ofcontrast enhancement categories. In instances where the analyzed one ormore region of contrast enhancement characteristics match one or morepreviously stored region of contrast enhancement characteristics, thecomputer system may determine one or more possible risk characteristicsand/or treatment options of the one or more region of contrastenhancement. In some embodiments, a user may be able to determine one ormore possible risk characteristics and/or treatment options of the oneor more region of contrast enhancement based on each of their one ormore region of contrast enhancement characteristics. In someembodiments, method 200 may continue at step 232 by sending datacorresponding to the one or more 3D brain lesion images to a 3D printingdevice. Based on the received data, the 3D printing device may create a3D physical representation or printed model of a region of contrastenhancement. In some embodiments, the 3D physical representation mayexhibit one or more of the region of contrast enhancementcharacteristics. A user may use the 3D physical representation as anadditional tool to help the user determine characteristics of the regionof contrast enhancement of for patient or healthcare provider education.

FIGS. 3A-C depict exemplary 2D MRI images 300 that are currently used todetect and analyze brain lesion characteristics. 2D MRI images mayprovide multiple views from different imaging angles. For example, atleast one sagittal image, at least one axial image, and at least onecoronal image may be provided. In many traditional methods, 2D MRIimages have been used to diagnose the existence of brain lesions anddetermine lesion characteristics. Before a patient receives the 2D MRI,the patient can be administered a contrast substance that will enter thebloodstream and travel to the brain lesion. As shown in FIG. 3A, a 2DMRI image depicts a brain lesion 304 (specifically, a glioblastoma tumorin this figure) prior to the administration of the contrast substance.The brain lesion 304 can be clearly distinguished from unaffected braintissue 308 (shown in various shades of gray and black). Eventually, asshown in FIG. 3B, the contrast substance 312 (shown as a bright, whitesubstance) is able to leak out of a blood vessel in a specific part(s)of the brain lesion 304 and collects in different concentrations indifferent areas of the brain lesion 304. In this way, the contrastsubstance 312 clearly delineates specific boundaries of certain regionsof contrast enhancement 316 of the brain lesion 304 from other portionsof the brain lesion and unaffected brain tissue 308. Once the regions ofcontrast enhancement 316 of the brain lesion 304 are isolated from theother portions of the brain lesion and unaffected brain tissue 308 inthe 2D MRI image, certain characteristics of the brain lesion can bedetermined, such as brain lesion dimensions (e.g., length 320 and width324), as shown in FIG. 3C. However, the analysis that can be performedon the brain lesion using such 2D MRI images is limited.

As such, in the disclosed embodiments, one or more MRI images 400 from a3D series may be received by a computing system from an MRI systemhaving 3D imaging capabilities, as shown in FIGS. 4A-B. In theembodiment shown, sagittal images 404 and axial images 408 are shown.Similar to the images shown in FIGS. 3A-C, the MRI images 400 illustratethe contrast substance 412 (depicted in one or more shades of white) ascontrasted with non-contrast enhancing brain lesion tissue 416 andhealthy brain tissue 420 (depicted in one or more shades of gray). Asshown, the contrast substance 412 clearly delineates the boundaries ofcertain regions of contrast enhancement of the brain lesion 424 fromnon-contrast enhancing portions of the brain lesion 416 and from healthyportions of the brain tissue 420. This may assist a user viewing theimage to distinguish regions of contrast enhancement of brain lesionsfrom other brain lesion areas and from healthy brain tissue. In someembodiments, 3D MRI images may be configured to be accessed and/ormanipulated by a user. In some embodiments, the user may be able torotate, zoom, mark, color, and select areas of the 3D MRI images. Insome embodiments, a computer system may perform a segmentation processon 3D MRI images that may segment the 3D MRI images into one or more ROIrepresenting a brain lesion. A brain lesion may be selected by thecomputer system or the user and may be denoted or saved as a ROI.

FIGS. 5A-B depict exemplary 3D brain lesion images 500 that may beconstructed according to an embodiment of the disclosure. In theembodiment shown, the 3D brain lesion image 504 shown in FIG. 5Acorresponds to the brain lesion shown in the ROI of FIG. 4A and the 3Dbrain lesion image 508 shown in FIG. 5B corresponds to the brain lesionshown in the ROI of FIG. 4B. In the embodiment shown, 3D brain lesionimages 500 may depict a brain lesion 512 and one or more brain lesioncharacteristics, such as a round protuberance 516 existing on aparticular surface of the brain lesion or a hole 520 disposed throughthe mass of the brain lesion. In some embodiments, 3D brain lesionimages 500 may be an MIP image. In some embodiments, 3D brain lesionimages 500 may be an orthogonal image generated from one or moreselected ROI that provides orthogonal views to a user. 3D brain lesionimages 500 may also be in stereolithography format. In some embodiments,3D brain lesion images 500 may provide a 3D spatial visualization of thebrain legion and may be configured to be accessed and/or manipulated bya user. In some embodiments, the user may be able to rotate, zoom, mark,color, and select areas of the 3D brain lesion images 500. As discussedabove, one or more brain lesion characteristics may be displayed. Insome embodiments, data related to lesion characteristics such as shape,size (e.g., x, y, and z planes), volume, surface area, surface area tovolume ratio, volume to surface area ratio, topographical surfacecharacteristics, geometric characteristics, thickness of the contrastenhanced shell, and the like may be acquired from the 3D MRI images 400by the computer system in order to depict the brain lesions 504, 508 andtheir brain lesion characteristics as accurately as possible. Due to theaccuracy of the 3D brain lesion images 500, the computer system and/orthe user may more efficiently and accurately determine characteristicsof the brain lesion that may be significant in determining categories ofrisk and treatment of the brain lesion.

FIGS. 6A-B depict exemplary 3D brain lesion images 600 showing variousregions of contrast of the depicted post-contrast brain lesionsaccording to an embodiment of the disclosure. In the embodiment shown,the 3D brain lesion image 604 shown in FIG. 6A corresponds to the 3Dbrain lesion image 504 shown in FIG. 5A and the 3D brain lesion image608 shown in FIG. 6B corresponds to the 3D brain lesion image 508 shownin FIG. 5B. In the embodiment shown, different areas of contrast may beillustrated by different colors and/or intensities. For example, in the3D brain lesion image 604 of FIG. 6A, one region of contrast 612(falling outside of the dashed and dash-dotted lines) corresponds to anouter shell of the contrast enhanced lesion and may be represented in ared color. A region of contrast 616 (bounded by the dashed line) thatcorresponds to the inner region of the contrast enhanced lesion may berepresented in an orange color, and a region of contrast 620 (bounded bythe dash-dotted line) that corresponds to a hole in the outer shell maybe represented by a blue color. In the exemplary 3D brain lesion image608 of FIG. 6B, the region of contrast 612 (falling outside of theoutermost dashed line) representing the outer shell of the contrastenhanced lesion may be represented by a red color. Regions of contrast616 (falling between the outermost dashed line and the dash-dottedlines) representing other surface areas of the lesion may be representedby an orange and/or yellow color, indicating the inner region of thecontrast enhanced lesion. Multiple regions of contrast 620 (fallingwithin the dash-dotted lines) of the lesion may be represented by a bluecolor, indicating that the lesion has more holes or imperfections by theouter shell than the lesion shown in FIG. 6A. Regions of contrast 616represent areas within the inner region of the contrast enhanced lesion.By representing different regions of contrast with different colors andintensities (and/or other visually-distinguishable parameters), thecomputer system and/or a user viewing the 3D brain lesion images candetermine important characteristics about the lesion that may becorrelated with growth areas or regions of recovery, treatment response,and risk factors of the lesion.

FIGS. 7A-8C depict exemplary experimental results from implementationsof the systems and methods described by the embodiments of thedisclosure. In order to test the disclosed embodiments, a series oftests were performed on a group of recruited patients. The inclusioncriteria for patient selection was: i) male or female patients>18 yearsof age with, ii) a confirmed diagnosis of GBM by histopathologyconsistent with WHO grade IV established criteria, and iii) recent brainMRI within the past 60 days demonstrating gadolinium enhancement.Exclusion criteria included: i) pregnant women, ii) prior or currentexposure to bevacizumab, iii) previous allergic, anaphylactoid, orintolerance to gadolinium-based contrast agents, iv) estimatedglomerular filtration rate (eGFR) of <30 mL/min/1.73 m²), and/or v)contraindications to MRI.

The recruited patients with a confirmed diagnosis of GBM were placedinto two groups. The patients in one group were clinically stablepatients without new or worsening neurological symptoms with clinicalstate verified by a board certified neuro-oncologist. The patients inthe other group exhibited new or worsening neurological symptomsunattenuated by glucocorticosteroid increase whose combined symptoms andradiologic changes on MRI warranted a change to their establishedtreatment plan due to probable clinical progression in congruence withestablished Response Assessment in Neuro-Oncology (RANO) responsecriteria for progressive disease.

Standardized brain MRI studies were performed on all study participantsand all analyses implemented without knowledge of clinical history,current or past treatments, or disease duration. All imaging studieswere performed on a 3T MRI scanner using a 32-channel phased array coilfor reception and body coil for transmission. Gadobutrol 0.1 ml/kg wasadministered at a rate of 2 cc/second. A five-minute delay was performedprior to the acquisition of post-contrast sequence data. Each MRI studyincluded one or more scout localizers, pre- and post-contrast 3Dhigh-resolution inversion recovery spoiled gradient-echo T1-weightedisotropic (1.0×1.0×1.0 mm³, TE/TR/TI=3.7/8.1/864 ms, flip angle 12degrees, 256×220×170 mm³ FOV, number of excitations (NEX)=1, 170 slices,duration: 4:11 min), a 3D fluid-attenuated inversion recovery (FLAIR)(1.1×1.1×1.1 mm³, TE/TR/TI=350/4800/1600 ms, flip angle 90 degrees,250×250×180 mm³ FOV, NEX=1, 163 slices, duration: 5:02 min), and 3D T2sequence acquired in sagittal plane (1.0×1.0×1.0 mm³,TE/TR/TI=229/2500/1600 ms, flip angle 90 degrees, 250×250×180 mm³ FOV,NEX=1, 164 slices, duration: 4:33 min). However, other types of MRIscanners and other 3D MRI imaging study parameters may be used, such asanisotropic protocols. Examples of these MRI images are image 704 shownin FIG. 7A and image 804 shown in FIG. 8A. The contrast substance 708,808 (shown as a bright, white substance) can be clearly distinguishedfrom unaffected brain tissue 712, 812 (shown in various shades of grayand black).

In the embodiments shown, post-contrast enhanced tumor imagesegmentation of the MRI images was performed using segmentation software(e.g., aySegmentation v1.00.004 plug-in of aycan OsiriX® PRO v3.00.008).However, other types and/or methods of image segmentation may be used.During segmentation, focal brain tumors were verified fromsimultaneously viewed 3D high-resolution pre- and post-contrastT1-weighted sequences in axial, coronal, and sagittal view. The contrastenhancing region and hypointense necrotic center of each tumor wereselected manually using a segmentation tool, and segmented lesions weresaved as specified regions of interest (ROI). All selected ROI fileswere exported into stereolithography (.stl) format, visually evaluatedusing 3D software (e.g., MeshLab, Visual Computing Lab—ISTI—CNR,v1.3.3), and exported for statistical analysis to a computing/processingsystem. One or more 3D image files were generated using the 3D isolationsoftware, allowing for 3D spatial visualization of the brain and lesionsof the selected ROI. However, other types and/or methods of 3D imagecreation may be used. Examples of these 3D lesion images are images 716shown in FIG. 7B and image 816 shown in FIG. 8B.

In some embodiments, identified post-contrast lesions depicted by the 3Dimages can be printed using a 3D printing device (e.g., MakerBot®Replicator 2× Experimental 3D unit with 1.75 mm acrylonitrile butadienestyrene (ABS) filament with a build platform temperature of 110° F. andan extruder temperature of 230° F.). Using fused filament fabrication, a200 μm layer resolution can be achieved with the printed files.Individual lesions can be printed at actual size and also enlarged basedon user preference. However, other types of 3D printers and/or othertypes of 3D printing technologies may be used to create 3D printed moldsof the identified post-contrast lesions. Other degrees of resolution mayalso be achieved.

As shown in FIGS. 7B and 8B, the 3D lesion images 716, 816, are isolatedinto various regions of contrast enhancement designated by differentcolor shades. For example, in images 716, 816, a region of contrastenhancement 720, 820 representing an outer shell of the tumor can beshown in a white color (in the figures, light grey). Similarly, a regionof contrast enhancement 724, 824 representing the inner aspect of thecontrast enhanced lesion can be shown in a pink and/or purple color (inthe figures, darker greys). In the embodiments shown, the segmentedcontrast enhancing outer shell (represented by region of contrastenhancement 720, 820) of each tumor was analyzed using principalcomponent analysis (PCA). The input data (X), representing all 3Dvertices on the segmented shell surface, was represented as a matrix (M)such that M=XX^(T) after zero-centering. The singular valuedecomposition (SVD) for M was calculated to find the highest ktheigenvalue and eigenvector. Three eigenvector outputs for each tumorwere acquired, representing the three orthogonal axes (e.g., axes 728,732, 736 in FIG. 7C and axes 828, 832, 836 in FIG. 8C, respectively),which maximize variance by projection. As shown in FIGS. 7C and 8C, thethree eigenvector outputs were analyzed as triangles 740, 840 in 3Dspace normalized to an equilateral triangle, and the shape variance ofeach tumor was computed. After rejecting equal variance based onBartlett's test, a two-sample t-test assuming unequal variance was usedto determine the significance of the difference between the variance ofthe two independent samples.

In the embodiments shown in FIGS. 7C and 8C, a medial axistransformation (MAT), which is a fundamental shape descriptor, was usedto represent the “skeleton” of the post-contrast enhanced lesion. Forevery point on the medial axis, the distance to at least two closestpoints on the lesion surface was stored as the radius of thecorresponding circle that occupied the space between the inner and outerboundaries of the contrast enhancing shell surface. The Q-MAT (quadraticmedial axis transformation) method was used for quadratic errorminimization to compute a compact representation of the MAT thatremained geometrically accurate, as well as remove insignificant,unstable branches to produce a piecewise linear approximation of the MATdata. The Q-MAT was used to extract the medial axis vertices of thesegmented contrast enhancing shell of the tumor and reduced the numberof vertices by a ratio of 0.05 with respect to the number of originalMAT vertices. In the embodiments shown in FIGS. 7C and 8C, a histogramof the MAT vertices was created to show the distribution of the MATvertices along each radius, corresponding to the 3D shell thickness ofeach tumor. The mean and variance of the radii were analyzed between thetwo clinical states using a two-sample t-test after verifying normalityof the two samples using a QQ-plot and verifying equality of varianceamong the two samples using Bartlett's test.

The distance of each 3D voxel in the contrast enhancing region to thenearest 3D surface mesh was calculated using a distance transform. Threedifferent distances were calculated per voxel corresponding with thedistance to three distinct surface meshes: i) the outside surfacerepresenting the outer shell of the contrast enhancing region, ii) theinner surface defined as the surface of the non-contrast enhancingregion within the contrast enhancing shell, and iii) the nearestdistance to either the outer or inner surface. In the embodiments shownin FIGS. 7D and 8D, a 3D distance transform and intensity histogram 744,844 was then created with the X axis 748, 848 representing the distancetransform value, the Y axis 752, 852 representing the intensity value,and the Z axis 756, 856 representing the number of voxels in eachtransform distance and intensity grid, respectively. The mean vector ofthe standard deviation of the distance and the standard deviation of theintensity were compared between the progressive and stable groups usingHotelling's T² after verifying the assumptions of multivariate normalityusing Royston's Test and equality of covariance matrices using Box'sTest. The mean vector of the average distance and the average intensitywere compared between the progressive and stable groups using a modifiedversion Hotelling's T² to account for evidence of unequal covariancematrices based on Box's test after verifying the assumptions ofmultivariate normality using Royston's Test.

As shown in FIGS. 6A and 6B, an isolated ROI was segmented into twoparts: contrast enhancing and non-contrast enhancing regionsrepresenting the outer and inner surfaces, respectively. The innersurface was further divided into two sections: i) the area covered bythe outer shell, and ii) the uncovered area, corresponding to aperforation in the 3D view of the outer surface shell. To compute thesetwo areas, the non-contrast enhancing region was first analyzed bycomputing a normal for every triangular face on its surface mesh. A rayextending from the center of each triangular face in the direction ofits surface normal was projected, allowing for an assessment ofintersections within the mesh of the outside contrast enhancing region.A triangular area was identified as an uncovered region and added to thetotal uncovered area, if the ray failed to intersect with the outsidemesh. A coverage ratio representing the amount of perforations in thecontrast enhanced shell surface was calculated by dividing the totaloutside region area by the sum of the total outside region area and thetotal uncovered area of the inside region.

The performance of the presence of incomplete coverage of the contrastenhanced surface in predicting the clinical state was evaluated usingtrue positives (near complete coverage of the contrast enhanced surfacein a patient with clinical worsening), false positives (near completecoverage of the contrast enhanced surface in clinically stablepatients), true negatives (incomplete coverage of the contrast enhancedsurface in clinically stable patients), and false negatives (incompletecoverage of the contrast enhanced surface in patients with clinicalworsening) to determine sensitivity, specificity, and positivepredictive values with 95% confidence intervals (CI). A 2-tailedFisher's exact test was used for analysis of the contingency tables. A pvalue≤0.05 was considered significant for all statistical testsperformed.

The study cohort was comprised of 15 GBM patients (male: 11 (73%);median age (range): 62 years (36-72)) with a median disease duration of6 months (range: 2-24 months). Hypermethylation of MGMT was present in 5patients (33.3%) and wild type IDH1 mutations identified in 10 (66.7%).The majority of patients (80%) received concurrent treatment withtemozolamide and chemoradiation. When comparing the demographic andclinical characteristics of the study cohort, the only significantdifference between groups involved treatment history. The baselinedemographic and clinical characteristics of the entire study cohort andby clinical state are summarized in TABLE 1, shown below:

TABLE 1 Demographic and clinical characteristic data of the studycohort. Total (n) 15 Age, median years (range) 62 (36-72) Sex male, n(%) 11 (73) female, n (%) 4 (27) Ethnicity Hispanic, n (%) 1 (7)Non-Hispanic, n (%) 14 (93) Disease Duration, median months (range) 6(2-24) Promoter of MGMT¹ unmethylated, n (%) 5 (33.3) methylated, n (%)1 (6.7) unknown, n (%) 9 (60) IDH1² Mutation wildtype, n (%) 10 (66.7)mutated, n (%) 1 (6.7) unknown, n (%) 4 (26.7) Concurrent Chemotherapywith Chemoradiation temozolamide, n (%) 12 (80) Other, n (%) 3 (20) Timesince last radiation treatment 4 (0.53-19) at 3D scan, median months(range) ¹O⁶-methylguanine-DNA-methyltransferase ²Isocitratedehydrogenase 1

An analysis of potential deviations in shape between clinical groups wasperformed by studying the 3D contrast enhancing outer shell of alltumors. Results from the PCA data, normalized to exclude the impact ofsize, revealed distinct morphological variances between groups (p=0.005)with more non-spherical, asymmetric shapes identified in the group thatwas clinically stable (8/9 (88%)). Lower orthogonal axis distancevariances of 4.77-10.70 associated with greater shape symmetry,encompassed all patients with clinical progression along with a singleclinically stable patient. Overall, the acquired 3D data provided moreinformation than conventional 2D information and distinct sphericalshapes exhibiting symmetry were appreciated in those subjects withreduced clinical stability (as shown, for example, in the shapes of the3D lesion images of FIGS. 5A, 6A, and 7B) as compared to those who weremore clinically stable (as shown, for example, in the shapes of the 3Dlesion images of FIGS. 5B, 6B, and 8B).

Medial axis transformation data aimed at evaluating the uniformity ofthe radial distances, indicating shell width, revealed more dynamicisosurface differences in patients with clinical progression. For thosepatients who were clinically stable, higher probability values forsmaller radii distances were observed, suggesting greater consistency inthe thickness and an overall thinner contrast enhancing shell. However,analyzing the mean and variance of the MAT radii of the two clinicalstates demonstrated no significant difference between groups (p=0.38 andp=0.13 respectively).

By analyzing the relationship between the outer and inner shell surfaceareas, percent coverage data was computed to represent the fullness ofthe outer shell with lower values below 1.0 being associated with agreater amount of perforations within the shell. All patients withclinical progression were found to have coverage ratios>0.951. Thepredictive value of a post-contrast enhanced shell that was moreuniformly full to clinical progression was determined with a sensitivityof 66.7% (95% CI 29.9-92.5), specificity of 100% (54.1-100), and PPV of100% (p=0.028, 2-tailed Fisher's exact test) (as shown in the shape ofthe 3D lesion image of FIG. 6A).

The intensity of each voxel in the contrast enhancing region wasanalyzed in relation to the distance of that voxel from both the outerand inner surfaces and 3D histograms were created to visualize the data.Qualitative differences were not observed when comparing the histogramdata. In addition, an evaluation of the mean and variance data regardingthe degree of contrast enhancement by distance and the observedintensity did not reveal significant differences between groups (p=0.36and p=0.32, respectively). When looking at the mean intensities betweenclinical groups alone, results trended more towards significance(p=0.10).

The effective management of patients with GBM requires an evaluation ofclinical and neuroradiological data and the timely administration ofavailable treatments. Differentiating the impact of therapeutic effects(i.e. tumor resection, chemoradiation, anti-neoplastic agents) and theinnate immune response following such interventions from tumorprogression based on alterations in acute blood brain barrier physiologymay be difficult to determine, requiring additional time for declarationof disease advancement to be apparent before a change in treatment ismade.

The 3D radiological characteristics corresponding with diseaseprogression identified within the most physiologically active area ofthe tumor are consistent with the known pathophysiology of GBM. As GBMtumor cells in the absence of an applied stimulus are able toeffectively deform their cellular shape to migrate outwards, uniformdispersion would be expected, resulting in 3D post contrast features tobe more spherical in shape and the outer shell of the enhanced region tobe fuller with reduced perforations. In contrast, the physiologicalresponse in neurologically stable patients would be expected to be moretailored to specific areas of increased inflammation when tumor growthis limited by prescribed treatments. The corresponding structure wouldbe expected to be more non-spherical and elongated in shape with adecreased propensity for circumferential fullness, and a moredisconnected 3D radiological pattern.

Analysis of the thickness of the contrast enhanced shell, the 3Dreflection of the 2D width of the affected tissue, demonstrated nosignificant difference between the defined groups. This observation maybe related to the reduced number of subjects studied and a trueassociation may ultimately exist. The current data suggest thatpost-contrast radiological changes may not be indicative of diseaseprogression. However, when observed in clinical practice or describedwithin formal MRI reports, this feature commonly represents a keyinflection point in patient management. Of note, the growth of thenon-contrast enhancing necrotic center of GBM tumors was found to befaster relative to the growth of the contrast enhancing ring. Therefore,it is plausible that the 3D width of the contrast enhancing region wouldremain constant despite tumor progression.

The value of signal intensity data, acquired from different imagingsequences on MRI, has been extensively explored in relation to highgrade gliomas. Ring enhancing gliomas on T1 post-contrast sequences wereoften found to have corresponding mixed signal intensity centrally witha hypointense peripheral arc on T2 weighted imaging, differentiatinghigh grade gliomas from other ring enhancing tumor types. In relation toassessing tumor recurrence, a general increase in intensityheterogeneity obtained from T2-weighted sequences was found todifferentiate progression from pseudoprogression in GBM patients.Similarly, an increase in FLAIR signal intensity within the resectioncavity post grand total resection (GTR) or subtotal resection (STR)normalized to the intensity of CSF correlated with tumor recurrence inhigh grade gliomas. In contrast, decreased FLAIR signal intensity withinthe peritumoral region post-resection predicted future progression ofthe infiltrative disease, and the amount of signal intensity reductionwas found to be directly proportional to the likelihood of tumorrecurrence. Signal intensity differences specifically focused on 3D T1post-contrast data from GBM patients have not been previously described.After analyzing the acquired intensity data in relation to distance andrelative frequency, no significant differences between clinical stateswere found. However, when evaluating the intensity frequencies alonebetween groups, there appeared to be a trend towards significance withgreater mean intensity values seen in neurologically unstable patients.

By implementing this disclosed systems and methods, the application of3D technology with post-contrast imaging data may inform healthcareproviders with new insights into disease states based on spatial,surface, and structural patterns, extending beyond constrained 2D views,that may allow for surveillance approaches to evolve and for treatmenttransitions to occur more quickly. The disclosed systems and methods mayalso be used to assess for approved or research based treatments and forpredicting future disease activity and clinical outcomes.

3D imaging capabilities provided by the present systems and methods mayallow for tracking of tumor behavior (e.g., distinguishing tumorprogression and pseudoprogression, assessing tumor response to therapy,and/or the like), shed light on the reasons for such behavior, and/orpredict such behavior, to an extent that is not provided by, or is notreadily appreciable from, conventional review of 2D MRI images.

For example, FIG. 9 depicts a method 900 for using 3D representations ofa lesion to identify one or more changes in one or more of the lesion'scharacteristics over time and, in some instances, predicting futurechange(s) in those characteristic(s). Method 900 may include a step 904of obtaining a first 3D representation of a lesion at a first time and asecond 3D representation of the lesion at a second time that is laterthan the first time. The first and second 3D representations may beobtained as described above (e.g., steps 208-220 of method 200) andmay—but need not—be images (e.g., method 900 and similar methods requirecomparison, but not necessarily visualization, of the first and second3D representations). Time elapsed between the first and second times maybe of any suitable duration, such as a matter of days, months, or thelike. In some instances, one or more therapies may be administered tothe patient during that duration; in this way, the present methods maybe used to evaluate the efficacy of those therap(ies).

To illustrate, FIGS. 10A and 10B are sagittal MRI images 1000 and 1008of a lesion 1004, each taken from a respective set of 3D MRI data. The3D MRI data associated with MRI image 1000 was taken at a first time,and the 3D MRI data associated with MRI image 1008 was taken at asecond, later time. From each of the sets of 3D MRI data, a 3Drepresentation of the lesion was created as described above.

At step 908, the first 3D representation may be compared to the second3D representation to identify one or more changes in one or more of thelesion's characteristics as indicated in the first and second 3Drepresentations. The characteristic(s) may include any of thosedescribed above, such as the lesion's shape, geometry, size, topology,volume, surface area, and the like, whether of its outer shell (e.g.,720, FIG. 7B), inner aspect (e.g., 724, FIG. 7B), or both. And suchcharacteristic(s) may each be quantified in any suitable fashion,including in one of the ways described above (e.g., a PCA- or MAT-basedquantification, percent coverage, shell thickness, or the like) oranother, whether as one value (e.g., a volume or surface area) or anarray of values (e.g., representing the characteristic across a surfaceof or throughout the lesion).

The method of identifying the change(s) may depend on the characteristicbeing investigated and how it is quantified. As a straightforwardexample, a surface area, volume, or other characteristic quantified asone value as indicated in the first 3D representation may be directlycompared to the same as indicated in the second 3D representation. Forfurther example, in instances where the characteristic is quantified asan array of values, changes in that characteristic between the first andsecond 3D representations may be identified—in some instances,themselves in an array—by contrasting corresponding values in the arrayassociated with the first 3D representation and the array associatedwith the second 3D representation.

At step 912, a third 3D representation of the lesion indicative of thechange(s) may be created; this step is particularly useful in situationsin which the change(s) are identified across a surface of or throughoutthe lesion. Such indication of the change(s) in the third 3Drepresentation may be via, for example, color or othervisually-distinguishable parameter (e.g., if the third 3D representationis an image), an animation, an array of values, or the like. In somemethods, the third 3D representation and the second 3D representationmay not be consecutively created. To illustrate, the third 3Drepresentation may be prepared simultaneously with the second 3Drepresentation. In some methods, the third 3D representation maycomprise the second 3D representation and an indication of the lesioncharacteristic change(s) between the first and second 3D representations(e.g., the second 3D representation, colored to indicate thosechange(s)).

Provided by way of illustration, FIG. 11 depicts a third 3Drepresentation 1100 that is indicative of changes betweencharacteristics of a lesion indicated in a first 3D representation ofthe lesion associated with a first time (lesion 1004 at the time of FIG.10A) and a second 3D representation of the lesion associated with asecond, later time (lesion 1004 at the time of FIG. 10B). In thisexample, the characteristics under investigation included the lesion'sshape, geometry, size, and topology. Those characteristics werequantified, in each of the first and second 3D representations, as anarray of distances, each from a center common to both the first andsecond 3D representations to a surface of the lesion's outer shell,across that surface. And, to identify the changes in thosecharacteristics, differences between corresponding values in the arraysof distances—displacements—were calculated. In FIG. 11 , suchdisplacements are indicated in color, with yellow (in the figures,lighter grey) regions 1104 (e.g., falling within the bold dashed lines)being the largest. As can be seen, this third 3D representation providesmore detailed information regarding changes to the lesion'scharacteristics than would be readily appreciated through conventionalstudy of 2D MRI images.

In some methods, at step 916, the change(s) can be used to predictfuture lesion behavior. For example, considering at least the durationbetween the first and second times, the change(s) can be projected toapproximate how the investigated characteristic(s) may change in thefuture. Provided by way of illustration, FIGS. 12A and 12B are MRIimages 1200 and 1208 of a lesion 1204, each taken from a respective setof 3D MRI data. The 3D MRI data associated with MRI image 1200 was takenat a first time, and the 3D MRI data associated with MRI image 1208 wastaken at a second, later time. From each of the sets of 3D MRI data, a3D representation of the lesion was created as described above. Bycomparing the first and second 3D representations, changes in thelesion's characteristics were identified (e.g., step 908 of method 900).Those changes were then projected to approximate the lesion'scharacteristics at a third time, later than the second time; thatapproximation is shown in simulated MRI image 1212 of FIG. 12C. FIG. 12Dis an actual MRI image 1216 of lesion 1204 at the third time. Bycomparing FIGS. 12C and 12D, it can be seen that the simulated andactual lesions have a similar shape and morphology.

It may be appreciated that the functions described above may beperformed by multiple types of software applications, such as webapplications or mobile device applications. If implemented in firmwareand/or software, the functions described above may be stored as one ormore instructions or code on a non-transitory computer-readable medium.Examples include non-transitory computer-readable media encoded with adata structure and non-transitory computer-readable media encoded with acomputer program. Non-transitory computer-readable media includesphysical computer storage media. A physical storage medium may be anyavailable medium that can be accessed by a computer. By way of example,and not limitation, such non-transitory computer-readable media cancomprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage,magnetic disk storage or other magnetic storage devices, or any otherphysical medium that can be used to store desired program code in theform of instructions or data structures and that can be accessed by acomputer. Disk and disc includes compact discs (CD), laser discs,optical discs, digital versatile discs (DVD), floppy disks and Blu-raydiscs. Generally, disks reproduce data magnetically, and discs reproducedata optically. Combinations of the above are also included within thescope of non-transitory computer-readable media. Moreover, the functionsdescribed above may be achieved through dedicated devices rather thansoftware, such as a hardware circuit comprising custom VLSI circuits orgate arrays, off-the-shelf semiconductors such as logic chips,transistors, or other discrete components, all of which arenon-transitory. Additional examples include programmable hardwaredevices such as field programmable gate arrays, programmable arraylogic, programmable logic devices or the like, all of which arenon-transitory. Still further examples include application specificintegrated circuits (ASIC) or very large scale integrated (VLSI)circuits. In fact, persons of ordinary skill in the art may utilize anynumber of suitable structures capable of executing logical operationsaccording to the described embodiments.

The above specification and examples provide a complete description ofthe structure and use of illustrative embodiments. Although certainembodiments have been described above with a certain degree ofparticularity, or with reference to one or more individual embodiments,those skilled in the art could make numerous alterations to thedisclosed embodiments without departing from the scope of thisinvention. As such, the various illustrative embodiments of thedisclosed methods, devices, and systems are not intended to be limitedto the particular forms disclosed. Rather, they include allmodifications and alternatives falling within the scope of the claims,and embodiments other than those shown may include some or all of thefeatures of the depicted embodiment. For example, components may becombined as a unitary structure and/or connections may be substituted.Further, where appropriate, aspects of any of the examples describedabove may be combined with aspects of any of the other examplesdescribed to form further examples having comparable or differentproperties and addressing the same or different problems. Similarly, itwill be understood that the benefits and advantages described above mayrelate to one embodiment or may relate to several embodiments.

The claims are not intended to include, and should not be interpreted toinclude, means-plus- or step-plus-function limitations, unless such alimitation is explicitly recited in a given claim using the phrase(s)“means for” or “step for,” respectively.

The invention claimed is:
 1. A system for creating 3-dimensional (3D)representations of post-contrast enhanced brain lesions, the systemcomprising: a computer system comprising at least one processorconfigured to: receive one or more 3D images of a brain; enable anidentification of one or more regions of contrast enhancement in the oneor more 3D images of the brain; enable a segmentation of the one or more3D images, the segmentation enabling an isolation of the one or moreregions of contrast enhancement; enable a creation of one or more 3Dregion of contrast enhancement images based on the segmentation, the oneor more 3D region of contrast enhancement images comprising one or moreregion of contrast enhancement characteristics; enable a comparison ofthe one or more region of contrast enhancement characteristics of theone or more 3D region of contrast enhancement images with one or morepredetermined region of contrast enhancement characteristics; and enablea determination of a category of the one or more regions of contrastenhancement based on a match between the one or more region of contrastenhancement characteristics of the one or more 3D region of contrastenhancement images and the one or more predetermined region of contrastenhancement characteristics.
 2. The system of claim 1, the computersystem further configured to enable sending of 3D representation datacorresponding to the one or more 3D region of contrast enhancementimages, the 3D representation data configured to enable a creation ofone or more physical 3D representations of the one or more regions ofcontrast enhancement.
 3. The system of claim 1, where the one or more 3Dimages comprise one or more maximum intensity projection (MIP) images,the MIP images configured to enable 3D spatial visualization of theregions of contrast enhancement.
 4. The system of claim 1, where the oneor more 3D region of contrast enhancement images comprise one or moreorthographic projections in stereolithographic format.
 5. The system ofclaim 2, where the one or more physical 3D representations comprisefused filament 3D printed models.
 6. The system of claim 1, where theone or more 3D images of the brain comprise one or more isotropicmagnetic resonance imaging images.
 7. The system of claim 1, where theone or more region of contrast enhancement characteristics comprise oneor more of geometric characteristics, surface characteristics, andstructural characteristics.
 8. The system of claim 1, where the one ormore predetermined region of contrast enhancement characteristicscorrespond to one or more lesion characteristics associated with one ormore disease risk factors.
 9. The system of claim 1, where the at leastone processor is further configured to use machine learning to generateat least one description for the one or more region of contrastenhancement characteristics and associate the at least one descriptionwith the category of the one or more regions of contrast enhancement.10. A method of creating 3-dimensional (3D) representations ofpost-contrast enhanced brain lesions, the method comprising: receiving,by a computer system comprising at least one processor, one or more 3Dimages of a brain; enabling, by the computer system, an identificationof one or more regions of contrast enhancement in the one or more 3Dimages of the brain; enabling, by the computer system, a segmentation ofthe one or more 3D images, the segmentation enabling an isolation of theone or more regions of contrast enhancement; enabling, by the computersystem, a creation of one or more 3D region of contrast enhancementimages based on the segmentation, the one or more 3D region of contrastenhancement images comprising one or more region of contrast enhancementcharacteristics; enabling, by the computer system, a comparison of theone or more region of contrast enhancement characteristics of the one ormore 3D region of contrast enhancement images with one or morepredetermined region of contrast enhancement characteristics; andenabling, by the computer system, a determination of a category of theone or more regions of contrast enhancement based on a match between theone or more region of contrast enhancement characteristics of the one ormore 3D region of contrast enhancement images and the one or morepredetermined region of contrast enhancement characteristics.
 11. Themethod of claim 10, further comprising enabling, by the computer system,sending of 3D representation data corresponding to the one or more 3Dregion of contrast enhancement images, the 3D representation dataconfigured to enable a creation of one or more physical 3Drepresentations of the one or more regions of contrast enhancement. 12.The method of claim 10, where the one or more 3D images comprise one ormore maximum intensity projection (MIP) images, the MIP imagesconfigured to enable 3D spatial visualization of the regions of contrastenhancement.
 13. The method of claim 10, where the one or more 3D regionof contrast enhancement images comprise one or more orthographicprojections in stereolithographic format.
 14. The method of claim 11,where the one or more physical 3D representations comprise fusedfilament 3D printed models.
 15. The method of claim 10, where the one ormore 3D images of the brain comprise one or more isotropic magneticresonance imaging images.
 16. The method of claim 10, where the one ormore region of contrast enhancement characteristics comprise one or moreof geometric characteristics, surface characteristics, and structuralcharacteristics.
 17. The method of claim 10, where the one or morepredetermined region of contrast enhancement characteristics correspondto one or more lesion characteristics associated with one or moredisease risk factors.
 18. The method of claim 10, the method furthercomprising using machine learning to generate at least one descriptionfor the one or more region of contrast enhancement characteristics andassociate the at least one description with the category of the one ormore regions of contrast enhancement.